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2019 Fuzzy Day Speakers
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Ron Alquist, Ronen Israel, and Tobias Moskowitz,


Fact, Fiction, and the Size Effect. Examine the multitude of size effect   and aim to clarify some of the misunderstanding surrounding it by performing simple tests using publicly available data.”



Ron Alquist is a Vice President and quantitative researcher on the Global Alternative Premia team at AQR. In that role, he conducts long-term research on topics related to multi-factor style premia, hedge fund behavior, and mutual fund flows, among other things. He is currently working on crowding in factor styles. His research has been published in a variety of practitioner and academic journals, including the Journal of Portfolio Management, the Journal of International Economics, and the Journal of Monetary Economics. Prior to AQR, he worked as an economist at Kings Peak Asset Management and the Bank of Canada. He has PhD in economics from the University of Michigan.



In the earliest days of empirical work in academic finance, the size effect was the first market anomaly to challenge the standard asset pricing model and prompt debates about market efficiency. The notion that small stocks have higher average returns than large stocks, even after risk-adjustment, was a path breaking discovery, one that for decades has been taken as an unwavering fact of financial markets. In practice, the discovery of the size effect fueled a crowd of small cap indices and active funds to a point where the investment landscape is now segmented into large and small stock universes. Despite its long and illustrious history in academia and its commonplace acceptance in practice, there is still confusion and debate about the size effect. We examine many claims about the size effect and aim to clarify some of the misunderstanding surrounding it by performing simple tests using publicly available data.


The paper is available here


Jennifer Bender, PhD, Director of Research, State Street Global Advisors


Asset Allocation vs. Factor Allocation- Can we build a unified Method? "


(Published in the December 2018 Journal of Portfolio Management)


Jennifer Bender, Ph.D., is a Senior Managing Director at State Street Global Advisors and Director of Research for the Global Equity Beta Solutions team. In this role, she is responsible for leading innovation and thought leadership at SSGA across key areas of index investing.  These research areas include core beta, smart beta, ESG and thematic investing.  Jenn joined SSGA in 2014 and since then, has been responsible for promoting the thought leadership of SSGA’s indexing capabilities.  Previously, Jenn was a Vice President in the Index and Analytics Research teams at MSCI. She began her career as an economist at DRI in 1996 and has held research roles at State Street Associates and Harvard University. Jenn holds MS and PhD degrees in Economics from Brandeis University. Her work has been published extensively in industry-leading journals and books such as the Institutional Investor Journals and Wiley Finance Series. She is on the Editorial Board of the Journal of Portfolio Management and a member of the Chicago Quantitative Alliance.






Stefano Giglio, "Hedging macroeconomic and financial uncertainty and volatility"



Stefano Giglio is a Professor of Finance at Yale SOM. His research interests span several topics, including asset pricing, macroeconomics, and real estate, with a particular focus on volatility risk and on the term structure of asset prices across markets. He has been awarded several prizes, including the AQR Insight Award, the Fama-DFA Prize for the Best Paper in the Journal of Financial Economics, and the Jacob Gold & Associates Best Paper Prize. His work has been featured in several news outlets, including Forbes and the Economist.



This paper studies the pricing of shocks to uncertainty and realized volatility using options contracts directly related to the state of the macroeconomy and of financial markets. Contracts that provide protection against shocks to macroeconomic uncertainty have historically earned statistically and economically significantly positive excess returns. If uncertainty shocks were viewed as bad by investors – in the sense of being associated with high marginal utility – portfolios that hedge them should instead earn negative premia. Portfolios exposed to the realization (as opposed to the expectation) of large shocks to fundamentals, on the other hand, have historically earned large and negative risk premia. These results imply that it is large realizations of shocks to fundamentals, not forward-looking uncertainty shocks, that drive investors’ marginal utility; in turn, these implications can be used to guide and discipline the role of volatility in macroeconomic models.


Huseyin Gulen, "Predictability of Stock Returns by Priced Scaled Variables"


Huseyin Gulen is a Professor of Finance at the Krannert Graduate School of Management at Purdue University, where he has been a faculty member since 2007. He received a Ph.D. degree in Finance from Purdue University in 2001. Prior to joining the Krannert faculty in 2007, Professor Gulen was a visiting faculty member at the University of Michigan from 2006 to 2007 and a faculty member at Virginia Tech from 2001 to 2007. Professor Gulen’s research is focused on cross-sectional and time-series stock-return predictability, market anomalies, investor behavior, trading strategies, executive compensation, mutual funds, and effects of political/policy uncertainty. His publications have appeared in Journal of Finance (four times), Journal of Financial Economics (twice), Review of Financial Studies (three times), and Journal of Business. His research has received multiple awards, including the Jack Treynor Prize - Q Group in 2016, and has been frequently covered in the popular press with citations in the Wall Street Journal, the New York Times, and many others.



Using survey data on expectations of future stock returns, we recursively estimate the degree of extrapolative weighting in investors' beliefs (DOX). In an extrapolation framework, DOX determines the relative weight investors place on recent-versus-distant past returns. DOX varies considerably over time, and the ability of price-scaled variables to predict the year-ahead equity premium is contingent on DOX. High price-scaled variables are followed by lower returns only when the DOX is high. Our findings support extrapolation-based theories of the aggregate stock market and the interpretation of price-scaled variables as proxies for mispricing. Our results help answer a critical question: when will an overvalued asset experience a correction?




Lira Mota, Hedging out unpriced risk in factor portfolios”



In the finance literature, a common practice is to create factor-portfolios by sorting on characteristics associated with average returns. We show that the resulting portfolios are likely to capture not only the priced risk associated with the characteristic, but also unpriced risk. We show that the unpriced risk can be hedged out of these factor-portfolios using covariance information estimated from past returns. We apply our methodology to hedge out unpriced risk in the Fama and French (2015)  five factor-portfolios. We find that the squared Sharpe-ratio of the optimal combination of the resulting hedged factor-portfolios is 2.26, compared with 1.21 for the unhedged portfolios.



Lira Mota is a PhD candidate in finance at Columbia Business School. Her area of research is empirical asset pricing and macro-finance. She holds a Master of Philosophy in Finance from Columbia Business School and a Master of Economics from Fundação Getúlio Vargas, Brazil. She won the best paper award in Finance from the Brazilian Econometric Society in 2015 and the Anbima Prize of Capital Markets for the best thesis project in finance in 2014.


The paper is available here


Igor Halperin


“Inverse Reinforcement Learning and Reinforcement learning models. How they differ from traditional financial models and how to incorporate them with portfolio optimization techniques.”

Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining NYU Tandon, Igor was an Executive Director of Quantitative Research at JPMorgan, and before that he worked as a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has also co-authored the book “Credit Risk Frontiers” published by Bloomberg LP. Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. 

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